Reference no: EM13357556
Multiple choices based on regression analysis.
1. A variable that cannot be measured in terms of how much or how many but instead is assigned values to represent categories is called
1. an interaction
2. a constant variable
3. a category variable
4. a qualitative variable
2. A variable that takes on the values of 0 or 1 and is used to incorporate the effect of qualitative variables in a regression model is
1. an independent variable
2. a dummy variable
3. an indicator variable
4. All of the above choices are correct
5. None of the above choices are correct
3. In regression analysis, the response variable is the
1. independent variable
2. dependent variable
3. slope of the regression function
4. Intercept
4. A multiple regression model has the form = 7 + 2 x1 + 9 x2. As x1 increases by 1 unit (holding x2 constant), y is expected to
1. increase by 9 units
2. decrease by 9 units
3. increase by 2 units
4. decrease by 2 units
5. A measure of goodness of fit for the estimated regression equation is the
1. multiple coefficient of determination
2. mean square due to error
3. mean square due to regression
4. sample size
6. The adjusted multiple coefficient of determination is adjusted for
1. the number of dependent variables
2. the number of independent variables
3. the number of equations
4. detrimental situations
7. In multiple regression analysis,
1. there can be any number of dependent variables but only one independent variable
2. there must be only one independent variable
3. The coefficient of determination must be larger than 1
4. there can be several independent variables, but only one dependent variable
8. Age and number of hours worked per week were used to predict GPA of students.
The following is the Excel printout.
Predict GPA of a 22-year old student who works 30 hours per week. Also, report what percentage of variability in GPA is explained by the multiple regression line.
SUMMARY OUTPUT
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Regression Statistics
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Multiple R
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0.85
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R Square
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0.72
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Adjusted R-Square
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0.43
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Standard Error
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0.48
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Observations
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5
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ANOVA
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df
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SS
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MS
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F
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Significance F
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Regression
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2
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1.16
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0.578088
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2.5365
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0.2827693
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Residual
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2
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0.46
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0.227912
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Total
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4
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1.61
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Coefficients
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Standard Error
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t Stat
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P-value
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Lower 95%
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Upper 95%
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Intercept
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0.96
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1.15
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0.835215
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0.4915
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-3.9999478
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5.926914
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Age
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0.2
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0.09
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2.228794
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0.1556
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-0.1834896
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0.577886
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hours
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-0.1
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0.04
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-2.14015
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0.1657
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-0.2892895
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0.097099
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1. GPA is 3.2 an variability explained is 72%
2. GPA is 3.2and variability explained is 85%
3. GPA is 2.36 and variability explained is 96%
4. GPA is 2.36, variability explained is 43%
9. Income and political affiliation of voters are used as independent variables to estimate donations made to a party during presidential election years. Voters who belong to these 3 parties are included in the study: Democrat, Republican, Green. How many independent variables would be included in a regression model to predict donation?
1. 2
2. 3
3. 4
4. 5
10. Data from several months for the following variables were used to generate a multiple regression equation for an automobile dealership:
Y= Sales (recorded in thousands of dollars)
X1= Advertising dollars (recorded in hundreds of dollars)
X2 = Number of Sales People
X3 = Location, South = 0, North = 1
The resulting equation is: Predicted Y = 50 + 18.5X1 + 9X2 - 20X3
Predict sales for the North location in a month where $1,500 dollars is spent in advertising, and four sales people are on payroll.
1. $27,816
2. $68,900
3. $343,500
4. $420,833